Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and bat SARS-like coronaviruses

  1. Rajanish Giri
  2. Taniya Bhardwaj
  3. Meenakshi Shegane
  4. Bhuvaneshwari R. Gehi
  5. Prateek Kumar
  6. Kundlik Gadhave
  7. Christopher J. Oldfield
  8. Vladimir N. Uversky

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1007/s00018-020-03603-x
  2. ScreenIT

    SciScore for 10.1101/2020.03.13.990598: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Molecular recognition features (MoRFs) determination in CoV proteomes: The authentic online bioinformatics predictors that use a different set of algorithms for the prediction of MoRFs were used: These include MoRFchibi_web [38], ANCHOR [39,40], MoRFPred [41], and DISOPRED3 [42].
    MoRFchibi_web
    suggested: None
    Software and Algorithms
    SentencesResources
    We have used Clustal Omega [27] for protein sequence alignment and Esprit 3.0 [
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    Esprit
    suggested: (ESPRIT, RRID:SCR_000552)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.03.13.990598v2 on bioRxiv
  4. Version published to 10.1101/2020.03.13.990598v1 on bioRxiv